Organic photovoltaic (OPV) materials offer a promising avenue toward cost-effective solar energy utilization. However, optimizing donor-acceptor (D-A) combinations to achieve high power conversion efficiency (PCE) remains a significant challenge. In this work, we propose a framework that integrates large-scale pretraining of graph neural networks (GNNs) with a GPT-2 (Generative Pretrained Transformer 2)-based reinforcement learning (RL) strategy to design OPV molecules with potentially high PCE. This approach produces candidate molecules with predicted efficiencies approaching 21\%, although further experimental validation is required. Moreover, we conducted a preliminary fragment-level analysis to identify structural motifs recognized by the RL model that may contribute to enhanced PCE, thus providing design guidelines for the broader research community. To facilitate continued discovery, we are building the largest open-source OPV dataset to date, expected to include nearly 3,000 donor-acceptor pairs. Finally, we discuss plans to collaborate with experimental teams on synthesizing and characterizing AI-designed molecules, which will provide new data to refine and improve our predictive and generative models.

This work presents a set of optimal machine learning (ML) models to represent the temporal degradation suffered by the power conversion efficiency (PCE) of polymeric organic solar cells (OSCs) with a multilayer structure ITO/PEDOT:PSS/P3HT:PCBM/Al. To that aim, we generated a database with 996 entries, which includes up to 7 variables regarding both the manufacturing process and environmental conditions for more than 180 days. Then, we relied on a software framework that brings together a conglomeration of automated ML protocols that execute sequentially against our database by simply command-line interface. This easily permits hyper-optimizing and randomizing seeds of the ML models through exhaustive benchmarking so that optimal models are obtained. The accuracy achieved reaches values of the coefficient determination (R2) widely exceeding 0.90, whereas the root mean squared error (RMSE), sum of squared error (SSE), and mean absolute error (MAE)>1% of the target value, the PCE. Additionally, we contribute with validated models able to screen the behavior of OSCs never seen in the database. In that case, R2~0.96-0.97 and RMSE~1%, thus confirming the reliability of the proposal to predict. For comparative purposes, classical Bayesian regression fitting based on non-linear mean squares (LMS) are also presented, which only perform sufficiently for univariate cases of single OSCs. Hence they fail to outperform the breadth of the capabilities shown by the ML models. Finally, thanks to the standardized results offered by the ML framework, we study the dependencies between the variables of the dataset and their implications for the optimal performance and stability of the OSCs. Reproducibility is ensured by a standardized report altogether with the dataset, which are publicly available at Github.

Picture of a polymer:non-fullerene acceptor solar cell device, for which the polymer was designed by machine learning. Researchers at Osaka University use machine learning to design and virtually test molecules for organic solar cells, which can lead to higher efficiency functional materials for renewable energy applications. Osaka University researchers employed machine learning to design new polymers for use in photovoltaic devices. After virtually screening over 200,000 candidate materials, they synthesized one of the most promising and found its properties were consistent with their predictions. This work may lead to a revolution in the way functional materials are discovered.

More than 200,000 candidate materials were virtually screened by the system at Osaka University in Japan. The team of researchers then synthesized one of the most promising, and found its properties were consistent with the system's predictions. Machine learning allows computers to make predictions about complex situations, as long as the algorithms are supplied with sufficient example data. This is especially useful for complicated problems in material science such as designing molecules for organic solar cells, the researchers said, as it can depend on a vast array of factors and unknown molecular structures. It could take humans years to sift data to find underlying patterns, and even longer to test all the possible candidate combinations of'donor' polymers and'acceptor' molecules that make up organic solar cells.

Imagine looking for the optimal configuration to build an organic solar cell made from different polymers. Does the active layer need to be very thick, or very thin? Does it need a large or a small amount of each polymer? Knowing how to predict the specific composition and cell design that would result in optimum performance is one of the greatest unresolved problems in materials science. This is, in part, due to the fact that the device performance depends on multiple factors.

If movies are to be believed, artificial intelligence is a one-way ticket to a dystopian future, with films like The Terminator, Blade Runner and The Matrix pointing to a bleak future for humanity – but new Australian research suggests AI could actually play a key role in avoiding the climate crisis. Australian researchers have unveiled a new artificial intelligence platform that has the potential to accelerate the development of cheaper and higher performance next generation solar cells, with the ability to discover new materials that do not exist yet. Researchers from the ARC Centre of Excellence in Exciton Science in Melbourne, have successfully demonstrated a new type of machine learning model that is able to predict the energy conversion efficiency of new materials, including those used in next generation organic solar cells. The model, developed by researchers based at RMIT University and Monash University, allows scientists to model'virtual materials' that do not yet exist, allowing progress towards the development of cheaper and higher performance solar cells to be fast-tracked. According to new research published in the journal Computational Materials, the new artificial intelligence platform is significantly faster than other machine learning programs, and its source code has been released freely for use by other researchers. The researchers believe the new model could help speed up the development of cheap and efficient organic solar cells, seen as a potentially cheaper alternative to traditional silicon based solar cells, but which have yet to achieve large-scale commercial deployment.